SUPER RESOLUTION INTRODUCTION

Transcription

1 SUPER RESOLUTION Jnanavardhini - Online MultiDisciplinary Research Journal Ms. Amalorpavam.G Assistant Professor, Department of Computer Sciences, Sambhram Academy of Management. Studies, Bangalore Abstract:- The Super Resolution(SR) reconstruction has become a hot research topic in the field of Image Processing. Super resolution is all about generating high-resolution image from low-resolution image. High resolution image provides a high pixel density therefore provides more information about the original image. High resolution images are very much important for computer vision applications for better performance for pattern recognition and analysis of images. It is useful in medical imaging for diagnosis. It is very much useful for processing of satellite images. Also it is useful for other applications. In this paper we have discussed the techniques used for obtaining super resolutions. It is implemented using MATLAB. Key words : MATLAB, Super resolution High Resolution INTRODUCTION In most digital imaging applications, high resolution images or videos are very much required for the purpose image processing and analysis. It plays a major role for two application areas such as the improvement of pictorial information for human interpretation and also used for representation for automatic machine perception. Image resolution provides the details contained in an image. Higher resolution images provide more image details. Classification of resolution The resolution of a digital image can be classified as follows: Pixel resolution Spatial resolution Spectral resolution Temporal resolution Radiometric resolution This paper mainly focused on spatial resolution Spatial resolution In digital image processing the image is made up of small picture element called pixels. Spatial resolution is the pixel density in an image and measures in pixels per unit area. The image spatial resolution is firstly limited by the following: 43

2 The imaging sensors or the imaging acquisition device : Modern image sensor is typically a charge-coupled device (CCD) or a complementary metaloxide-semiconductor (CMOS) active-pixel sensor. These sensors are typically arranged in a twodimensional array to capture two-dimensional image signals. The sensor size or equivalently the number of sensor elements per unit area in the first place determines the spatial resolution of the image to capture. Higher the density of the sensors results higher spatial resolution the imaging system. An imaging system with inadequate detectors will generate low resolution images with blocky effects, due to the aliasing from low spatial sampling frequency. In order to increase the spatial resolution of an imaging system, one straight forward way is to increase the sensor density by reducing the sensor size. However, as the sensor size decreases, the amount of light incident on each sensor also decreases, causing the so called shot noise. Also, the hardware cost of sensor increases with the increase of sensor density or correspondingly image pixel density. Therefore, the hardware limitation on the size of the sensor restricts the spatial resolution of an image that can be captured. The optics in the acquisition device: While the image sensors limit the spatial resolution of the image, the image details (high frequency bands) are also limited by the optics, due to lens blurs (associated with the sensor point spread function (PSF)), lens aberration effects, aperture diffractions and optical blurring due to motion. Constructing imaging chips and optical components to capture very highresolution images is prohibitively expensive and not practical in most real applications, e.g., widely used surveillance cameras and cell phone built-in cameras. Camera speed and hardware storage: In some other scenarios such as satellite imagery, it is difficult to use high resolution sensors due to physical constraints. Another way to address this problem is to accept the image degradations and use signal processing to post process the captured images, to trade off computational cost with the hardware cost. Super- Resolution (SR) reconstruction Super-resolution (SR) are techniques that construct high-resolution (HR) images from several observed low-resolution (LR) images, thereby increasing the high frequency components and removing the degradations caused by the imaging process of the low resolution camera. The basic idea behind SR is to combine the non-redundant information contained in multiple lowresolution frames to generate a high-resolution image. A closely related technique with SR is the single image interpolation approach, which can be also used to increase the image size. However, since there is no additional information provided, the quality of the single image 44

3 Interpolation is very much limited due to the ill-posed nature of the problem, and the lost frequency components cannot be recovered. In the SR setting, however, multiple low-resolution observations are available for reconstruction, making the problem better constrained. The non-redundant information contained in these LR images is typically introduced by sub pixel shifts between them. These sub pixel shifts may occur due to uncontrolled motions between the imaging system and scene, e.g., movements of objects, or due to controlled motions, e.g., the satellite imaging system orbits the earth with predefined speed and path. Each low-resolution frame is a decimated, aliased observation of the true scene. SR is possible only if there exists sub pixel motions between these low resolution frames1, and thus the ill-posed up sampling problem can be better conditioned. In the imaging process, the camera captures several LR frames, which are down sampled from the HR scene with sub pixel shifts between each other. SR construction reverses this process by aligning the LR observations to sub pixel accuracy and combining them into a HR image grid The basic idea for super-resolution reconstruction is from multiple low-resolution Frames. Sub pixel motion provides the complementary information among the Low-resolution frames that makes SR reconstruction possible and thereby overcoming the imaging limitation of the camera. Super Resolution application areas 1. Surveillance video: frame freeze and zoom region of interest (ROI) in video for human perception, resolution enhancement for automatic target recognition (e.g. try to recognize a criminal's face). 2. Remote sensing: several images of the same area are provided, and an improved resolution image can be sought. 3. Medical imaging (CT, MRI, Ultrasound etc): several images limited in resolution quality can be acquired, and SR technique can be applied to enhance the resolution. 4. Video standard conversion, e.g. from NTSC video signal to HDTV signal. Techniques for Super Resolution Blurs and noise of the image should be removed to give a high resolution image. We should use denoising techniques for this purpose. Now we focused on the techniques used. De-noising Techniques Image de-noising stands as a relatively separate part of image processing. The fundamental task of de-noising is to deconvolve the blurred image with the PSF that exactly describes the distortion. Deconvolution is the process of reversing the effect of convolution. The quality of the de-noising image is mainly determined by knowledge of the PSF. De-noising is an interactive process. You might need to repeat the de-noising process multiple times. For all 45

4 the iteration the parameters of the de-noising function must be varied. This should be repeated till you achieve an image that, based on the limits of your information, is the best approximation of the original scene. The de-noising, or degradation, of an image can be caused by many factors: Movement during the image captures process, by the camera or, when long exposure times are used, by the subject. Out-of-focus optics, use of a wide-angle lens, atmospheric turbulence, or a short exposure time, which reduces the number of photons captured. Scattered light distortion in con-focal microscopy. A blurred or degraded image can be approximately described by this equation g the blurred image g=hf+n, where H the distortion operator also called the point spread function (PSF). In the spatial domain, the PSF describes the degree to which an optical system blurs (spreads) a point of light. The PSF is the inverse Fourier transform of the optical transfer function (OTF). In the frequency domain, the OPT describes the response of a linear, position-invariant system to an impulse. The OPT is the Fourier transform of the point spread function (PSF). The distortion operator, which convolved with the image, creates the distortion. Distortion caused by a point spread function is just one type of distortion. F The original true image, N Additive noise, introduced during image acquisition that corrupts the image. Implementation in MATLAB: The Jackson image is taken for measurement. The following operations are done on the image. Simulation of a motion blurs Restoration using PSF Computation of noise spectrum Simulation of a picture with motion blur and noise Restoration using ACF Restoration using NP and ID-ACF I=imread('d:\matlab704\work\jackson.bmp'); figure;imshow(i); title('i/p:input IMAGE'); %step 2 : Simulate a Motion blur LEN=31; THETA=11; PSF=fspecial('motion',LEN, THETA); Blurred=imfilter(I,PSF,'circular','conv'); 46

5 figure;imshow(blurred); title('blurred'); % Step 3 : Restore the Blurred Image wnr1=deconvwnr(blurred,psf); figure;imshow(wnr1); title('restored, True PSF'); % Step 6:Use Autocorrelation to improve Image Restoration noise=0.1*randn(size(i)); BlurredNoisy=imadd(Blurred,im2uint8(noise)); figure;imshow(blurrednoisy); title('blurred & Noisy'); NP=abs(fftn(noise)).^2; figure;mesh(np); title('noise Sepctrum'); NPOW=sum(NP(:))/prod(size(noise)); % noise power NCORR=fftshift(real(ifftn(NP))); %noise ACF, centered IP=abs(fftn(im2double(I))).^2; IPOW=sum(IP(:))/prod(size(I)); % original image power ICORR=fftshift(real(ifftn(IP))); % Image ACF, cenered wnr7=deconvwnr(blurrednoisy,psf,ncorr,icorr); figure;imshow(wnr7); title('restored with ACF'); ICORR1=ICORR(:,ceil(size(I,1)/2)); wnr8=deconvwnr(blurrednoisy,psf,npow,icorr1); figure;imshow(wnr8); title('restored with NP & ID-ACF'); RESULT AND SCREEN SHOTS The following operations are done on the image. Simulation of a motion blurs Restoration using PSF Computation of noise spectrum Simulation of a picture with motion blur and noise Restoration using ACF Restoration using NP and ID-ACF The result of the program provides the following. The Jackson image is taken as input image. Jnanavardhini - Online MultiDisciplinary Research Journal 47

6 I/P:INPUT IMAGE Jnanavardhini - Online MultiDisciplinary Research Journal Figure 1 :INPUT IMAGE Simulate a real-life image that could be blurred (e.g., due to camera motion or lack of focus). The example simulates the blur by convolving a Gaussian filter with the true image (using imfilter). The Gaussian filter then represents a point-spread function, PSF The image with simulated motion blur is as shown below. Blurred FIGURE 2 : BLURRED IMAGE The restored image with the point-spread function, PSF is as shown below. FIGURE 3 : RESTORED TRUE PSF The figure 3 of Noise spectrum after computation is as shown below: Noise Sepctrum

7 The noise shown above is added to blurred image, the resulting image is as shown below. Blurred & Noisy FIGURE 4 : BLURRED & NOISY Autocorrelation is used to improve image restoration, the resultant image is shown below. Restored, True PSF Restored with ACF 49

8 FIGURE 5 :RESTORED WITH ACF Correlation is done with image; noise power to improve image restoration, the resultant image is shown below. Restored with NP & ID-ACF CONCLUSION FIGURE 6 : RESTORED WITH NP & ID-ACF This paper presented an effective approach toward single image super-resolution based on spatial domain. Our progressive deconvolution approach can produce very promising results not only in synthetic experiments, but also in various types of real cases. Our results show our approach outperforms other state-of-the-art techniques and have wide applications in scientific and daily areas. Robotic deburring is the material removal process used to take burrs, sharp edges, or fins off metal parts. Burrs are almost always left on parts and tools that have been made Deburring Applications Can Effectively Remove Burrs.Thus our methods are very practical and effective for achieving satisfactory photos in dim light conditions using off-of-shelf hand-held camera. 50

9 REFERENCES [1] Temizel and T. Vlachos, Image resolution up scaling in the wavelet domain using directional cycle spinning, J. Electronic Letters, vol. 41, pp , [2] A.Jensen, A.la Courharbo, Ripples in Mathematics: The Discrete Wavelet Transform, springer, [3] D. Glasner, S. Bagon, and M. Irani. Super-resolution from a single image. In Proc. ICCV, [4] Gonzalez and Woods, Digital Image Processing, Pearson Education Asia. [5] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing Using MATLAP, Pearson Education, [2008] [6] Anil K.Jain, Fundamentals of Digital Image Processing, Prentice-Hall of India Private Limited